Summary of Learning with Posterior Sampling For Revenue Management Under Time-varying Demand, by Kazuma Shimizu et al.
Learning with Posterior Sampling for Revenue Management under Time-varying Demand
by Kazuma Shimizu, Junya Honda, Shinji Ito, Shinji Nakadai
First submitted to arxiv on: 8 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles the revenue management (RM) problem by developing an efficient algorithm for maximizing revenue through pricing items or services. The challenge lies in unknown demand distributions that vary over time, as seen in industries like airlines and retail. To address this issue, the authors introduce an episodic generalization of the RM problem and propose a posterior sampling-based algorithm to optimize prices via linear programming. This approach yields a Bayesian regret upper bound for general models with correlated demand parameters between time periods. The authors also derive a regret lower bound for generic algorithms. Experimental results show that their proposed algorithm outperforms benchmark methods and is comparable to an optimal policy in hindsight. A heuristic modification of the algorithm further improves pricing policy learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how businesses can make more money by setting good prices for things they sell or services they offer. The problem is that we don’t always know what people will want to buy, and this changes over time. The authors created a new way to solve this problem using math and computer algorithms. They tested their method and found it works well, even when compared to the best possible solution. This could help companies like airlines or stores make better decisions about pricing. |
Keywords
» Artificial intelligence » Generalization
